Inspired by Kolmogorov complexity [1]} (simpler objects have simpler “description”), which can be easily bounded by any compression algorithm, we use ZLIB  [2]} to compress the NNF, and record the compression ratio. This gives a diversity measure (in \([0,1]\) ) that we term NNFDIV. We can measure the RGB-similarity (NNFDIST) by recording the MSE of each generated patch to its nearest-neighbor. In tab:comparison we report the results of these metrics on two diverse video generation datasets (sec:videodatasetcomparisonappendix). As seen in the table, in both cases SinFusion has the best trade-off in terms of diversity and similarity. We also report SVFID [3]} that measures similarity. while VGPNN is expected to have the best score (since it is copying chunks of frames), our score is between SinganGIF and HP-VAE-GAN. We attribute this to patch outliers in the generations (as apparent from the results), however similarity is not our main goal but the generation of novel motions.
